The proposed model can be contrasted with previously introduced latent variable models used in multi-view learning and dimensionality reduction. Factor analysis (FA) [1]} is a classical method that is a generalization of Principal Component Analysis (PCA) [2]} and Probabilistic PCA [3]}. FA decomposes the observed data matrix into a low dimensional set of factor loadings and factor scores, imposing a low-rank constraint on the covariance matrix. Like our proposed model, the FA model also assumes a low-dimensional Gaussian latent space but it does not account for the counting nature of the observed data.
